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  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.10 
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    Abstract
    Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
    Type
    a
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.06 
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    Abstract
    This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
    Type
    a
  3. Warner, A.J.: Natural language processing (1987) 0.06 
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
    Type
    a
  4. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.05 
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    Date
    8.10.2000 11:52:22
    Source
    Library science with a slant to documentation. 28(1991) no.4, S.125-130
    Type
    a
  5. McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.05 
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    Source
    Computational linguistics. 22(1996) no.2, S.217-248
    Type
    a
  6. Somers, H.: Example-based machine translation : Review article (1999) 0.05 
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    Date
    31. 7.1996 9:22:19
    Type
    a
  7. New tools for human translators (1997) 0.05 
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    Abstract
    A special issue devoted to the theme of new tools for human tranlators
    Date
    31. 7.1996 9:22:19
  8. Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.05 
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    Date
    28. 2.1999 10:48:22
    Type
    a
  9. Byrne, C.C.; McCracken, S.A.: ¬An adaptive thesaurus employing semantic distance, relational inheritance and nominal compound interpretation for linguistic support of information retrieval (1999) 0.04 
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    Date
    15. 3.2000 10:22:37
    Type
    a
  10. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.04 
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    Date
    1. 3.2013 14:56:22
  11. Hutchins, J.: From first conception to first demonstration : the nascent years of machine translation, 1947-1954. A chronology (1997) 0.04 
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    Abstract
    Chronicles the early history of applying electronic computers to the task of translating natural languages, from the 1st suggestions by Warren Weaver in Mar 1947 to the 1st demonstration of a working, if limited, program in Jan 1954
    Date
    31. 7.1996 9:22:19
    Type
    a
  12. Basili, R.; Pazienza, M.T.; Velardi, P.: ¬An empirical symbolic approach to natural language processing (1996) 0.03 
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    Abstract
    Describes and evaluates the results of a large scale lexical learning system, ARISTO-LEX, that uses a combination of probabilisitc and knowledge based methods for the acquisition of selectional restrictions of words in sublanguages. Presents experimental data obtained from different corpora in different doamins and languages, and shows that the acquired lexical data not only have practical applications in natural language processing, but they are useful for a comparative analysis of sublanguages
    Date
    6. 3.1997 16:22:15
    Type
    a
  13. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.03 
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    Abstract
    AutoSlog is a system that addresses the knowledge engineering bottleneck for information extraction. AutoSlog automatically creates domain specific dictionaries for information extraction, given an appropriate training corpus. Describes experiments with AutoSlog in terrorism, joint ventures and microelectronics domains. Compares the performance of AutoSlog across the 3 domains, discusses the lessons learned and presents results from 2 experiments which demonstrate that novice users can generate effective dictionaries using AutoSlog
    Date
    6. 3.1997 16:22:15
    Type
    a
  14. Haas, S.W.: Natural language processing : toward large-scale, robust systems (1996) 0.03 
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    Abstract
    State of the art review of natural language processing updating an earlier review published in ARIST 22(1987). Discusses important developments that have allowed for significant advances in the field of natural language processing: materials and resources; knowledge based systems and statistical approaches; and a strong emphasis on evaluation. Reviews some natural language processing applications and common problems still awaiting solution. Considers closely related applications such as language generation and th egeneration phase of machine translation which face the same problems as natural language processing. Covers natural language methodologies for information retrieval only briefly
    Type
    a
  15. Wanner, L.: Lexical choice in text generation and machine translation (1996) 0.03 
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    Date
    31. 7.1996 9:22:19
    Type
    a
  16. Morris, V.: Automated language identification of bibliographic resources (2020) 0.03 
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    Date
    2. 3.2020 19:04:22
    Type
    a
  17. Kay, M.: ¬The proper place of men and machines in language translation (1997) 0.03 
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    Abstract
    Machine translation stands no chance of filling actual needs for translation because, although there has been progress in relevant areas of computer science, advance in linguistics have not touched the core problems. Cooperative man-machine systems need to be developed, Proposes a translator's amanuensis, incorporating into a word processor some simple facilities peculiar to translation. Gradual enhancements of such a system could lead to the original goal of machine translation
    Content
    Reprint of a Xerox PARC Working Paper which appeared in 1980
    Date
    31. 7.1996 9:22:19
    Footnote
    Contribution to a special issue devoted to the theme of new tools for human translators
    Type
    a
  18. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.03 
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    Abstract
    The Specialized Information Services Division (SIS) of the National Library of Medicine (NLM) provides Web access to more than a dozen scientific databases on toxicology and the environment on TOXNET . Search queries on TOXNET often include misspelled or variant English words, medical and scientific jargon and chemical names. Following the example of search engines like Google and ClinicalTrials.gov, we set out to develop a spelling "suggestion" system for increased recall and precision in TOXNET searching. This paper describes development of dictionary technology that can be used in a variety of applications such as orthographic verification, writing aid, natural language processing, and information storage and retrieval. The design of the technology allows building complex applications using the components developed in the earlier phases of the work in a modular fashion without extensive rewriting of computer code. Since many of the potential applications envisioned for this work have on-line or web-based interfaces, the dictionaries and other computer components must have fast response, and must be adaptable to open-ended database vocabularies, including chemical nomenclature. The dictionary vocabulary for this work was derived from SIS and other databases and specialized resources, such as NLM's Unified Medical Language Systems (UMLS) . The resulting technology, A-Z Dictionary (AZdict), has three major constituents: 1) the vocabulary list, 2) the word attributes that define part of speech and morphological relationships between words in the list, and 3) a set of programs that implements the retrieval of words and their attributes, and determines similarity between words (ChemSpell). These three components can be used in various applications such as spelling verification, spelling aid, part-of-speech tagging, paraphrasing, and many other natural language processing functions.
    Date
    14. 8.2004 17:22:56
    Source
    Online. 28(2004) no.3, S.22-29
    Type
    a
  19. Schwarz, C.: THESYS: Thesaurus Syntax System : a fully automatic thesaurus building aid (1988) 0.03 
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    Abstract
    THESYS is based on the natural language processing of free-text databases. It yields statistically evaluated correlations between words of the database. These correlations correspond to traditional thesaurus relations. The person who has to build a thesaurus is thus assisted by the proposals made by THESYS. THESYS is being tested on commercial databases under real world conditions. It is part of a text processing project at Siemens, called TINA (Text-Inhalts-Analyse). Software from TINA is actually being applied and evaluated by the US Department of Commerce for patent search and indexing (REALIST: REtrieval Aids by Linguistics and STatistics)
    Date
    6. 1.1999 10:22:07
    Type
    a
  20. Paolillo, J.C.: Linguistics and the information sciences (2009) 0.03 
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    Abstract
    Linguistics is the scientific study of language which emphasizes language spoken in everyday settings by human beings. It has a long history of interdisciplinarity, both internally and in contribution to other fields, including information science. A linguistic perspective is beneficial in many ways in information science, since it examines the relationship between the forms of meaningful expressions and their social, cognitive, institutional, and communicative context, these being two perspectives on information that are actively studied, to different degrees, in information science. Examples of issues relevant to information science are presented for which the approach taken under a linguistic perspective is illustrated.
    Date
    27. 8.2011 14:22:33
    Type
    a

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